Update

app.py

@@ -99,17 +99,17 @@ def filter_models(
     print(f"Initial df shape: {df.shape}")
     print(f"Initial df content:\n{df}")
 
-    # Model Type フィルタリング
+    # Filter by model type
     type_emoji = [t.split()[0] for t in type_query]
     df = df[df["T"].isin(type_emoji)]
     print(f"After type filter: {df.shape}")
 
-    # Precision フィルタリング
+    # Filter by precision
     df = df[df["Precision"].isin(precision_query)]
     print(f"After precision filter: {df.shape}")
 
-    # Model Size フィルタリング
-    # When `df` is empty, `size_mask` is empty, and the shape of `df[size_mask]` becomes (0, 0).
+    # Filter by model size
+    # Note: When `df` is empty, `size_mask` is empty, and the shape of `df[size_mask]` becomes (0, 0)
     if len(df) > 0:
         size_mask = df["#Params (B)"].apply(
             lambda x: any(x in NUMERIC_INTERVALS[s] for s in size_query if s != "Unknown")
@@ -119,19 +119,19 @@ def filter_models(
         df = df[size_mask]
     print(f"After size filter: {df.shape}")
 
-    # Add Special Tokens フィルタリング
+    # Filter by special tokens setting
     df = df[df["Add Special Tokens"].isin(add_special_tokens_query)]
     print(f"After add_special_tokens filter: {df.shape}")
 
-    # Num Few Shots フィルタリング
+    # Filter by number of few-shot examples
     df = df[df["Few-shot"].astype(str).isin(num_few_shots_query)]
     print(f"After num_few_shots filter: {df.shape}")
 
-    # Version フィルタリング
+    # Filter by evaluator version
     df = df[df["llm-jp-eval version"].isin(version_query)]
     print(f"After version filter: {df.shape}")
 
-    # Vllm Version フィルタリング
+    # Filter by vLLM version
     df = df[df["vllm version"].isin(vllm_query)]
     print(f"After vllm version filter: {df.shape}")